A New Framework for Sign Language Recognition based on 3D Handshape Identification and Linguistic Modeling

Mark Dilsizian, Polina Yanovich, Shu Wang, Carol Neidle, Dimitris Metaxas


Abstract
Current approaches to sign recognition by computer generally have at least some of the following limitations: they rely on laboratory conditions for sign production, are limited to a small vocabulary, rely on 2D modeling (and therefore cannot deal with occlusions and off-plane rotations), and/or achieve limited success. Here we propose a new framework that (1) provides a new tracking method less dependent than others on laboratory conditions and able to deal with variations in background and skin regions (such as the face, forearms, or other hands); (2) allows for identification of 3D hand configurations that are linguistically important in American Sign Language (ASL); and (3) incorporates statistical information reflecting linguistic constraints in sign production. For purposes of large-scale computer-based sign language recognition from video, the ability to distinguish hand configurations accurately is critical. Our current method estimates the 3D hand configuration to distinguish among 77 hand configurations linguistically relevant for ASL. Constraining the problem in this way makes recognition of 3D hand configuration more tractable and provides the information specifically needed for sign recognition. Further improvements are obtained by incorporation of statistical information about linguistic dependencies among handshapes within a sign derived from an annotated corpus of almost 10,000 sign tokens.
Anthology ID:
L14-1096
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
1924–1929
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/1138_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Mark Dilsizian, Polina Yanovich, Shu Wang, Carol Neidle, and Dimitris Metaxas. 2014. A New Framework for Sign Language Recognition based on 3D Handshape Identification and Linguistic Modeling. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 1924–1929, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
A New Framework for Sign Language Recognition based on 3D Handshape Identification and Linguistic Modeling (Dilsizian et al., LREC 2014)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/1138_Paper.pdf